Lead AI Engineer, Forward Deployed

Company Description NeoAistriq is an AI-native business consulting and technology firm that helps small and midsize businesses improve customer acquisition, service, and retention, and redesign work across teams. The company builds connected applications and AI-enabled workflows, with core services in AI Adoption & Operations, Customer Growth & Operations, Agentic Operations, and Product & AI Engineering. NeoAistriq is an OpenAI Select Partner and an ElevenLabs Partner, grounding technology choices in customer fit and practical outcomes. The firm works across professional and technology services, education and membership, SaaS and digital products, and gaming and esports, informed by products and operations within Fractal KX. Based in Frisco, Texas, NeoAistriq focuses on conversational experiences and community-native business to deliver accountable, principal-led solutions.

You will work directly with our founder, team, and customers to turn ambiguous requirements into working solutions.

This role combines forward deployed engineering, business analysis, applied AI engineering, and full stack development. You should be comfortable asking questions, mapping workflows, making technical decisions, writing code, and owning delivery.

You will use AI extensively in your own engineering workflow and build AI capabilities into the solutions we deliver. We value people who learn quickly, adapt thoughtfully, and ship useful improvements frequently.

What you will do

Understand problems and define solutions

* Work with stakeholders to understand their goals, workflows, constraints, and pain points.

* Translate conversations into clear requirements, acceptance criteria, and manageable delivery milestones.

* Evaluate when to build, buy, integrate, or simplify.

* Deliver the smallest useful solution, then improve it through real user feedback.

Build AI systems and agent harnesses

* Build applications and workflows using different AI models and providers.

* Engineer the systems around models: instructions, context, retrieval, tool use, state, execution loops, and human approvals.

* Implement structured outputs, retries, fallbacks, checkpoints, and failure recovery.

* Evaluate model choices against quality, reliability, latency, privacy, and cost.

* Create practical evaluations and monitoring to identify regressions and improve results.

Work with MCPs and integrations

* Connect, configure, build, and debug Model Context Protocol (MCP) clients and servers.

* Design clear tool schemas and reliable interactions with external systems.

* Handle authentication, authorization, credentials, and tool permissions appropriately.

* Integrate APIs, webhooks, CRMs, communication platforms, and business applications.

Own engineering and delivery

* Build and maintain frontend, backend, database, and integration components.

* Use AI coding tools effectively while reviewing and verifying generated code.

* Deploy solutions, investigate failures, and support users through adoption.

* Maintain clear documentation, reusable components, and engineering handoffs.

* Communicate progress, tradeoffs, risks, and decisions in plain language.

What we are looking for

* Strong hands-on software engineering experience, with evidence of shipping and maintaining production applications.

* Proficiency in Python and/or TypeScript, plus the ability to work across a modern full stack.

* Practical experience building with LLM APIs, tool calling, structured outputs, and AI workflows.

* Experience working with MCPs, including troubleshooting tool execution and authentication.

* Understanding of agent harness design, context management, retrieval, and evaluation.

* Strong fundamentals in APIs, databases, authentication, Git, testing, deployment, and debugging.

* Ability to understand business processes and turn incomplete requirements into working software.

* Evidence of learning unfamiliar technologies quickly and applying them effectively.

* Clear communication, sound judgment, and ownership from discovery through delivery.

You should be able to explain what you built, why you chose that approach, how you verified it, and what happened after users started using it.

Helpful additional experience

* Customer-facing engineering, technical consulting, or forward deployed delivery.

* CRM and enterprise platforms such as Salesforce, HighLevel, HubSpot, or similar systems.

* Conversational applications, voice agents, and community platforms such as Discord.

* Multi-tenant applications, access controls, and secure handling of customer data.

* Model routing, observability, inference cost optimization, and reusable integration patterns.

* Working in a startup or directly with a founder.

What success looks like

During your first 90 days, you will:

* Understand our priority use cases, existing systems, and delivery practices.

* Own one meaningful project from discovery through deployment and user feedback.

* Establish appropriate evaluations, logging, and failure handling for that solution.

* Create reusable components or integrations that accelerate future work.

* Make routine technical decisions independently and surface important tradeoffs early.

How we work

We value small delivery milestones, direct communication, practical experimentation, and dependable execution. You will have room to choose tools and approaches, with accountability for the outcome.

Fast shipping means getting useful software into users’ hands, learning from it, and improving it without accumulating avoidable reliability or security problems.

How to apply

Share your résumé or professional profile and two or three examples of relevant work on leadership@neoaistriq.com. For each example, briefly explain:

* The business problem and your personal contribution.

* The models, tools, MCPs, or integrations you used.

* The key technical decisions and how you verified the solution.

* The outcome, limitations, and what you learned.

GitHub repositories, demos, architecture notes, and short walkthroughs are welcome. Please anonymize confidential work.

Our selection process will include a technical discussion and a practical exercise. You may use your preferred AI tools during the exercise.